# Routing AgentRFC

A dynamic routing agent for optimal model selection and orchestration

## Author(s)
Haim Barad
Madison Evans

## Status
Proposed

## Objective
Create an intelligent routing layer that:
- Analyzes text-based input queries in real-time.
- Selects optimal model based on criteria like cost, latency, and capability requirements
- Supports multiple cloud providers and self-hosted models

## Motivation
- Growing complexity of multi-LLM environments
- Need for cost-efficient inference without sacrificing quality
- Lack of standardized orchestration patterns
- Increasing demand for hybrid cloud/on-prem deployments

## Design Proposal
### Core Components:
1. Query Analyzer: Supports several known classifiers (matrix factorization, BERT, etc) and Semantic understanding and intent classification
2. Routing Engine: Provides dynamic model selection based on query complexity
3. Monitoring: Real-time metrics collection (latency, cost, accuracy)
4. This code is based on RouteLLM, which is available at https://github.com/lm-sys/RouteLLM

### Key Features:
- Dynamic model selection based on query complexity
- Returns the selected model endpoint so that developer can call proper model, or does actual routing to the chosen model so this process is invisible to the developer
- Cost-aware routing policies

## Miscellaneous
- Performance: <5ms overhead per request
- Security: Zero-trust authentication between components
- Staging Plan:
  1. Phase 1: Basic routing MVP
  2. Phase 2: Advanced analytics dashboard
  3. Phase 3: Auto-scaling integration